class_docstr: Convert graph shape from Channel last (NHWC) to Channel first (NCHW) format.
  
  Supported functions: `Convolution`, `Deconvolution`, `BatchNormalization`,
  `MaxPooling`, `AveragePooling`, `SumPooling`, `Unpooling`, `Concatenate`
  
  Args:
      inputs (list of nn.Variable): Original channel last version of very beginning inputs (NHWC) of a network.
      inputs_cf (list of nn.Variable): Channel first version of very beginning inputs (NCHW) of a network.
        If this is not given, `inputs_cf` are generated internally and holded.
  
  Examples:
  
  .. code-block:: python
  
     pred = Model(...)
  
     import nnabla.experimental.graph_converters as GC
  
     modifiers = [GC.ChannelFirstModifier(<inputs of pred>)]
     gc = GC.GraphConverter(modifiers)
     pred = gc.convert(pred)
code: def __init__(self, inputs, inputs_cf=None):
      super(ChannelFirstModifier, self).__init__()
  
      self._inputs = inputs
      self._inputs_cf = inputs_cf
  
      self._prepare_inputs(inputs, inputs_cf)
  
file_path: nnabla/python/src/nnabla/experimental/graph_converters/channel_first.py
imports: import nnabla
  import nnabla.functions
  import numpy
  from .graph_converter import FunctionModifier
label_desc: Use image node_id 1 to create a new ChannelFirstModifier object from inherited base classes: FunctionModifier with example: obj = ChannelFirstModifier(inputs, inputs_cf)